Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	85,151
2	South Dakota	74,919
3	Iowa	60,092
4	Wisconsin	57,474
5	Nebraska	52,523
6	Utah	48,590
7	Idaho	46,951
8	Illinois	46,289
9	Tennessee	45,790
10	Mississippi	45,326
11	Montana	45,005
12	Alabama	44,712
13	Arkansas	44,518
14	Louisiana	44,110
15	Kansas	42,802
16	Missouri	41,662
17	Rhode Island	41,461
18	Florida	41,431
19	Minnesota	40,973
20	Wyoming	40,073
21	Nevada	39,751
22	Oklahoma	39,640
23	Georgia	38,670
24	Indiana	38,518
25	South Carolina	38,187
26	Arizona	38,080
27	Texas	37,995
28	Alaska	33,173
29	New Jersey	31,894
30	Kentucky	31,845
31	New Mexico	31,215
32	North Carolina	30,064
33	Delaware	29,986
34	Colorado	29,327
35	New York	29,220
36	Michigan	28,892
37	Maryland	27,795
38	Massachusetts	27,629
39	District of Columbia	27,012
40	California	26,500
41	Connecticut	26,164
42	Ohio	26,123
43	Puerto Rico	23,994
44	Virginia	23,974
45	Pennsylvania	21,501
46	West Virginia	19,228
47	Washington	18,201
48	Oregon	13,667
49	Hawaii	11,802
50	New Hampshire	11,053
51	Maine	6,782
52	Vermont	4,820

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	1,873
2	South Dakota	1,488
3	Minnesota	1,399
4	Montana	1,186
5	Nebraska	1,150
6	Iowa	1,131
7	Wyoming	1,066
8	Utah	1,000
9	Indiana	999
10	Wisconsin	951
11	Alaska	904
12	Illinois	880
13	Tennessee	857
14	Kansas	848
15	Oklahoma	800
16	Missouri	796
17	Colorado	759
18	Rhode Island	753
19	Michigan	683
20	Ohio	651
21	Idaho	646
22	Nevada	553
23	New Mexico	548
24	West Virginia	524
25	Kentucky	479
26	Arkansas	445
27	Connecticut	433
28	New Jersey	416
29	Delaware	405
30	Pennsylvania	397
31	Alabama	381
32	Massachusetts	355
33	Texas	331
34	Mississippi	327
35	Maryland	323
36	Arizona	301
37	Florida	299
38	South Carolina	292
39	North Carolina	280
40	Washington	280
41	New Hampshire	269
42	California	236
43	Louisiana	220
44	New York	214
45	Oregon	214
46	Virginia	209
47	Puerto Rico	197
48	Georgia	188
49	District of Columbia	187
50	Vermont	141
51	Maine	118
52	Hawaii	46

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,866
2	New York	1,724
3	Massachusetts	1,500
4	Connecticut	1,334
5	Louisiana	1,320
6	Rhode Island	1,198
7	Mississippi	1,191
8	North Dakota	982
9	District of Columbia	935
10	Illinois	885
11	Arizona	865
12	Michigan	844
13	Georgia	823
14	Florida	817
15	South Carolina	804
16	Delaware	755
17	Arkansas	737
18	Pennsylvania	734
19	Indiana	733
20	South Dakota	727
21	Maryland	712
22	Texas	695
23	Alabama	662
24	Iowa	641
25	Nevada	623
26	New Mexico	589
27	Tennessee	568
28	Missouri	567
29	Minnesota	527
30	Montana	494
31	Ohio	491
32	Wisconsin	476
33	California	463
34	North Carolina	461
35	Colorado	452
36	Virginia	445
37	Kansas	434
38	Idaho	432
39	Nebraska	416
40	Kentucky	391
41	Oklahoma	388
42	New Hampshire	367
43	Washington	349
44	West Virginia	326
45	Puerto Rico	294
46	Wyoming	248
47	Utah	226
48	Oregon	183
49	Hawaii	156
50	Alaska	125
51	Maine	122
52	Vermont	94

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	28
2	North Dakota	15
3	Montana	14
4	Wyoming	9
5	Arkansas	8
6	Illinois	8
7	New Mexico	6
8	Iowa	5
9	Rhode Island	5
10	Michigan	4
11	Minnesota	4
12	Wisconsin	4
13	Colorado	3
14	Idaho	3
15	Indiana	3
16	Massachusetts	3
17	Oklahoma	3
18	Tennessee	3
19	West Virginia	3
20	Arizona	2
21	Connecticut	2
22	Mississippi	2
23	Nebraska	2
24	Nevada	2
25	New Jersey	2
26	North Carolina	2
27	Pennsylvania	2
28	Puerto Rico	2
29	South Carolina	2
30	Texas	2
31	Utah	2
32	Alabama	1
33	District of Columbia	1
34	Florida	1
35	Georgia	1
36	Kansas	1
37	Kentucky	1
38	Louisiana	1
39	Maryland	1
40	Missouri	1
41	New York	1
42	Ohio	1
43	Oregon	1
44	Washington	1
45	Alaska	0
46	California	0
47	Delaware	0
48	Hawaii	0
49	Maine	0
50	New Hampshire	0
51	Vermont	0
52	Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Norton	Kansas	185,600	1	99
Lincoln	Arkansas	181,741	2	99
Bon Homme	South Dakota	178,090	3	99
Chattahoochee	Georgia	177,776	4	99
Buffalo	South Dakota	169,215	5	99
Davidson	Tennessee	58,249	416	86
Richland	South Carolina	45,630	861	72
York	South Carolina	29,308	1886	39
Orange	California	21,356	2389	23
Pierce	Washington	16,227	2636	16

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,208	1	99
Jerauld	South Dakota	6,458	2	99
Hancock	Georgia	5,321	3	99
Emporia city	Virginia	5,238	4	99
Kenedy	Texas	4,950	5	99
Richland	South Carolina	688	1078	65
Davidson	Tennessee	558	1352	56
Orange	California	481	1536	51
York	South Carolina	431	1685	46
Pierce	Washington	318	2021	35

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.